Research Scientist

At the Ellison Institute of Technology (EIT), we’re on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, policy makers, and entrepreneurs to tackle humanity’s greatest challenges. 

We are looking for visionary Research Scientists in the Molecular Simulations and Design team who balance machine learning innovation with a mission-driven bias toward execution. 

Molecular Simulations and Design accelerates the design of biomolecules, with an initial focus on enzymes, by combining generative models, physical simulation, and learning from experimental data. You will push the frontier where these three meet. You will have the unique opportunity to translate your research innovations directly to experimental campaigns with a rapidly accelerating feedback cycle. We are particularly excited about physics-based methods working together with generative models to lead to generalizable and robust predictions of experimental outcomes and designs. 

• Innovative research in new methodologies e.g.,: Boltzmann samplers, long-timestep molecular dynamics, machine learned interatomic potentials, generative models (including flow matching, diffusion). Full-stack research, from pre-training to fine-tuning to design-build-test loops. 

• Key Challenge: Predictive simulation-based workflows, accelerated by machine learning. Merging physics and machine learning for robust generalization. Robust evaluations that translate to downstream impact on campaigns. Research innovations that go beyond performance on evals and have measurable acceleration of experimental campaigns.  

• Ideal Background: PhD in a relevant field with a track record of excellence at the intersection of machine learning, biomolecules and simulations. Expertise building simulation-based pipelines to predict experimental outcomes and using machine learning to accelerate those pipelines. Experience with production grade pipelines for small molecules, biomolecules design and simulations. Agentic engineering skills and ability to build and validate end-to-end machine learning pipelines and in-silico design-build-test loops. Ability to ship at a high velocity. 

• PhD in Machine Learning, AI, Computer Science, or a related quantitative field (Physics, Math, Computational Biology), or equivalent experience. 

• Research Excellence: A proven track record of impactful research demonstrated by e.g. publications in top-tier AI venues or scientific journals. 

• Technical Mastery: Deep expertise in deep learning frameworks and the ability to write high-quality, reproducible research code. 

• Mission Mindset: A strong desire to apply advanced research to solve tangible, real-world problems in a fast-paced environment. 

• Innovate & Lead: Conceptualize and develop original evals, training algorithms and fine-tuning recipes and connect them to ongoing design-build-test-loops 

• End-to-end impact: Translate your work to experimental campaigns with a rapid feedback cycle. 

• Publish & Present: Contribute to the global AI community through mentoring PhD students, co-authoring high-impact publications (NeurIPS, ICML, ICLR, etc.) and technical reports. 

• Collaborate: Partner with cross-disciplinary squads of experimentalists, scientific domain experts, data domain experts and infrastructure to quickly move innovation to impact.